As a QA Engineer working on automation frameworks at my company, the best part about GitHub Copilot is how it accelerates test automation by generating boilerplate code, edge‑case scenarios, and automation scripts far faster than writing everything from scratch. For example, while building Pytest suites for accessibility checks, Copilot suggested assertions, mocks, and fixtures that aligned perfectly with our framework, reducing setup friction and saving hours of manual effort.

Another major advantage is Copilot’s ability to use project context intelligently. When I’m working within our company’s test repositories, it surfaces alternative approaches — such as different ways to structure parameterized tests — so I can compare options and choose the most robust one for production pipelines. This context‑aware assistance ensures that the generated code is not only syntactically correct but also relevant to the specific QA workflows we maintain.

Overall, Copilot doesn’t replace my QA judgment; it strengthens it by cutting down repetitive effort and freeing me to focus on quality analysis, exploratory testing, and ensuring reliability across systems in a real workplace environment. Its consistency and context awareness make it a reliable partner in professional QA projects. Review collected by and hosted on G2.com.

From a QA perspective in my company’s automation projects, the biggest drawback of GitHub Copilot is that it can generate code that looks polished but isn’t always dependable, especially around edge cases or complex test scenarios. For example, while working on compliance‑driven Pytest suites logged in with my company account, Copilot often suggested repetitive or shallow test cases instead of highlighting coverage gaps. This meant I still had to spend time validating, adjusting, and refining its output before it was production‑ready.

Another challenge is that Copilot doesn’t consistently grasp the deeper intent behind QA work — such as robustness, negative testing, and regulatory compliance. Its suggestions can overlook the quality checks that matter most in enterprise pipelines. In day‑to‑day use, Copilot does speed up routine coding tasks, but it requires ongoing oversight to prevent subtle bugs and to maintain consistent standards across the entire test suite.

A concrete case: during a recent sprint, I used Copilot to generate parameterized test functions for API validation. While the code compiled correctly, it missed certain negative scenarios required for compliance. I had to manually supplement these cases to ensure full coverage, which slowed down the evaluation process.

Overall, Copilot’s cautiousness and occasional lack of depth mean that while it accelerates routine tasks, it still demands careful QA oversight to ensure reliability in professional environments Review collected by and hosted on G2.com.

GitHub Copilot helps speed up development by suggesting code snippets, methods, test cases, and logic in real time as I code. I find it especially useful for repetitive tasks, API implementations, and generating boilerplate.

What I personally like most is that it cuts down the time I spend searching for syntax or common implementation patterns, so I can stay focused on the business logic instead. Review collected by and hosted on G2.com.

The suggestions aren’t always accurate or well optimized, especially when dealing with project-specific logic or more complex enterprise use cases. At times it produces outdated approaches or incomplete code, so manual review and validation are still necessary.

In a few backend implementation scenarios, Copilot suggested methods that looked syntactically correct but didn’t fit our project architecture or align with our coding standards, which meant I still had to adjust and verify the output. Review collected by and hosted on G2.com.

As an AI automation and DevOps engineer, what I love most about GitHub Copilot is how it dramatically cuts down the cognitive load of switching between endless YAML configurations, CI/CD pipelines, and bash scripts. Instead of wasting time digging through documentation for standard boilerplate code or syntax rules, Copilot anticipates what I need and generates it instantly. This lets me stay in the zone and focus on the high-level architecture and security of the infrastructure, rather than the tedious syntax. Review collected by and hosted on G2.com.

From a DevOps and automation standpoint, what I dislike most is its tendency to confidently suggest outdated or legacy syntax for complex infrastructure-as-code and CI/CD pipelines. Because it lacks a holistic view of a multi-repository cloud architecture, it often generates code that works in isolation but violates specific security policies or network topologies. This requires me to spend extra time meticulously auditing its suggestions to prevent deployment failures or compliance risks. Review collected by and hosted on G2.com.

It provides a comprehensive interface for interacting with multiple LLMs at the same time, while abstracting away the complexity involved. It also includes several useful capabilities, such as planning, agent mode, tool calls, and more. Its performance is very good as well. It integrates well with existing vscode IDE for a unified experience. Pricing was extremely good until june. June onwards, they changed to pay per usage model and made claude models pricy. Still, its worth it. Very much a corner stone for our whole AI SDLC approach. Review collected by and hosted on G2.com.

Pricing model is very expensive based on June changes. Review collected by and hosted on G2.com.

GitHub Copilot has really improved my coding workflow. The UI easily gets integrated with Visual Studio Code feels smooth and easy to use. It gives fast AI code suggestions, help complete functions and save a lot of typing time. Performance is quick, thought sometime suggestions are inaccurate. Overall, it makes learning and coding faster and more productive. Review collected by and hosted on G2.com.

I'm currently using the student plan which was giving access to multiple AI models from different model providers (including Anthropic, Google, OpenAI...), but with the latest update to GitHub Copilot they just removed most flagship models and limit access to remaining, which is quite disappointing. Review collected by and hosted on G2.com.

What I like the most is that it saves me time on repetitive tasks. With FastAPI or Django, it suggests almost complete endpoints, models, and CRUD logic, which speeds things up quite a bit.

It also helps when I integrate APIs or write SQL queries: it's not always perfect, but it gives me a quick foundation to work from. Review collected by and hosted on G2.com.

What I don't like is that sometimes it suggests things that "seem correct," but aren't entirely so. With FastAPI, for example, it has suggested poorly constructed validations or dependencies that pass at first glance, but then fail in logic or security. Review collected by and hosted on G2.com.

It automatically corrected code ,installs requirements .txt file ,run python commands parallely as well as sequentially .also run commands in a virtual env if possible.it is very beneficial for coders have tons of files made and they donnot want to dig in for errors finding.just ask to GitHub copilot chat and it will make that sorted for you.Also it helps in running the total project from scratch from installing dependencies to testing project on corner test cases. Review collected by and hosted on G2.com.

Many a times when it don't find solution it not stops ,instead it go on a infinite loop checking the same condition again and again and hence wastage of cpu cycles and resources happen.also manyatimes it do not take user responses as they are said for.it automatically starts its computation if the user is simply asking to show file . Review collected by and hosted on G2.com.

The best part is how familiar the platform feels, especially for beginners, and the free credits are a nice bonus. You can use both online and offline models on it, and there are plenty of configuration options. Overall, it has really made writing code and building solutions much easier for me. Review collected by and hosted on G2.com.

There are very few plugins and MCPs available, and the options become even more limited when I try to use tools while working on code that’s on another server. This is especially noticeable when I’m connected to that server in VS Code through an SSH tunnel. Review collected by and hosted on G2.com.

I like how easy it is to just provide a prompt and have the work done in seconds. When needed, it can take my input and make changes directly in my file, instead of me having to go back and forth copying and pasting specific pieces of code. Review collected by and hosted on G2.com.

At times your intended task may not be done correctly until you look deep into the logic and find out that what it was supposed to do it didn't do instead it found a simpler hard coded way out. Review collected by and hosted on G2.com.

What I like most about GitHub Copilot is that it helps me stay focused while coding. Instead of spending time looking up syntax or writing repetitive code, I can concentrate on solving the actual problem. It's especially useful for generating boilerplate code, suggesting functions, and helping me learn new libraries or frameworks more quickly. I always review and understand the suggestions before using them, but overall it saves time and makes development much more efficient. Review collected by and hosted on G2.com.

One thing I dislike about GitHub Copilot is that its suggestions are not always accurate or optimized, especially for complex logic or unfamiliar codebases. Sometimes it confidently generates code that looks correct but contains subtle bugs or doesn't follow the project's coding style. It can also make it tempting to accept suggestions without fully understanding them, so I think it's important to review and test everything before using it. Overall, it's a great productivity tool, but it still requires human judgment. Review collected by and hosted on G2.com.